During evolution, plants have developed the ability to produce a vast array of specialized metabolites, which play crucial roles in helping plants adapt to different environmental niches. However, their biosynthetic pathways remain largely elusive. In the past decades, increasing numbers of plant biosynthetic pathways have been elucidated based on approaches utilizing genomics, transcriptomics, and metabolomics. These efforts, however, are limited by the fact that they typically adopt a target-based approach, requiring prior knowledge. Here, we present MEANtools, a systematic and unsupervised computational integrative omics workflow to predict candidate metabolic pathways de novo by leveraging knowledge of general reaction rules and metabolic structures stored in public databases. In our approach, possible connections between metabolites and transcripts that show correlated abundance across samples are identified using reaction rules linked to the transcript-encoded enzyme families. MEANtools thus assesses whether these reactions can connect transcript-correlated mass features within a candidate metabolic pathway. We validate MEANtools using a paired transcriptomic-metabolomic dataset recently generated to reconstruct the falcarindiol biosynthetic pathway in tomato. MEANtools correctly anticipated five out of seven steps of the characterized pathway and also identified other candidate pathways involved in specialized metabolism, which demonstrates its potential for hypothesis generation. Altogether, MEANtools represents a significant advancement to integrate multi-omics data for the elucidation of biochemical pathways in plants and beyond.

This record includes training materials associated with the Australian BioCommons webinar ‘Multivariate integration of multi-omics data with mixOmics’. This webinar took place on 6 March 2024. Event description Multi-omics data (eg. transcriptomics, proteomics) collected from the same set of biospecimens or individuals is a powerful way to understand the underlying molecular mechanisms of a biological system. mixOmics, a popular R package, integrates omics data from a wide range of sources into a single, unified view making it easier to explore and reveal interactions between omics layers. It overcomes many of the challenges of multi-omic data integration arising from data that are complex and large, with few samples (10,000), and generated using different technologies. Prof Kim-Anh Lê Cao, head of the mixOmics team, is delivering this webinar to outline the different methods implemented in mixOmics and how statistical data integration is defined in this context. She will demonstrate how these approaches are applied to analysis of different multi-omics studies and outline the latest methodological developments in this area. From a study of human newborns, to multi-omics microbiomes, and multi-omics in single cells, these examples illustrate how mixOmics is used to perform variable selection and identify a signature of omics markers that characterise a specific phenotype or disease status. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Speaker: Prof Kim-Anh Lê Cao, Director of Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne. Host: Dr Melissa Burke, Australian BioCommons Training materials Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. Mixomics_BioCommons: A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/5XpmQ5X89lA

In light of the rapid accumulation of large-scale omics datasets, numerous studies have attempted to characterize the molecular and clinical features of cancers from a multi-omics perspective. However, there are great challenges in integrating multi-omics using machine learning methods for cancer subtype classification. In this study, MoGCN, a multi-omics integration model based on graph convolutional network (GCN) was developed for cancer subtype classification and analysis. Genomics, transcriptomics and proteomics datasets for 511 breast invasive carcinoma (BRCA) samples were downloaded from the Cancer Genome Atlas (TCGA). The autoencoder (AE) and the similarity network fusion (SNF) methods were used to reduce dimensionality and construct the patient similarity network (PSN), respectively. Then the vector features and the PSN were input into the GCN for training and testing. Feature extraction and network visualization were used for further biological knowledge discovery and subtype classification. In the analysis of multi-dimensional omics data of the BRCA samples in TCGA, MoGCN achieved the highest accuracy in cancer subtype classification compared with several popular algorithms. Moreover, MoGCN can extract the most significant features of each omics layer and provide candidate functional molecules for further analysis of their biological effects. And network visualization showed that MoGCN could make clinically intuitive diagnosis. The generality of MoGCN was proven on the TCGA pan-kidney cancer datasets. MoGCN and datasets are public available at https://github.com/Lifoof/MoGCN. Our study shows that MoGCN performs well for heterogeneous data integration and the interpretability of classification results, which confers great potential for applications in biomarker identification and clinical diagnosis.

This dataset repository corresponds to the project Unsupervised neural network for single cell Multi-omics INTegration (UMINT): An application to health and disease.

R code for all analyses

The global multiomics market, valued at $3.11 billion in 2025, is projected to experience robust growth, exhibiting a compound annual growth rate (CAGR) of 15.26% from 2025 to 2033. This expansion is driven by several key factors. Advancements in sequencing technologies, particularly next-generation sequencing (NGS), are enabling researchers to analyze multiple omics datasets simultaneously, providing a more comprehensive understanding of complex biological systems. This holistic approach is proving invaluable in drug discovery and development, accelerating the identification of novel therapeutic targets and biomarkers. Furthermore, the increasing prevalence of chronic diseases, such as cancer and neurodegenerative disorders, is fueling demand for more precise diagnostic and therapeutic tools, bolstering the multiomics market. Growing investments in research and development across both academia and the pharmaceutical and biotechnology sectors further contribute to this market's rapid growth. The integration of artificial intelligence (AI) and machine learning (ML) in multiomics data analysis is also significantly impacting the field, enabling faster and more accurate interpretations of complex datasets. The market segmentation reveals significant opportunities across various product types, platforms, and applications. While instruments and reagents constitute major segments, the 'Other Products' category, encompassing software and data analysis tools, is experiencing rapid growth due to the increasing complexity of multiomics data. Single-cell multiomics, offering higher resolution and insights into cellular heterogeneity, is gaining traction over bulk multiomics. Within platforms, genomics maintains a dominant position, followed by transcriptomics and proteomics. However, integrated omics platforms, offering a more comprehensive analysis of multiple datasets simultaneously, are showing significant potential for future growth. Oncology and neurology are leading application areas, with substantial research focused on developing personalized medicine approaches leveraging multiomics data. The academic and research institutes segment remains a key end-user, while pharmaceutical and biotechnology companies are increasingly adopting multiomics for drug discovery and development, promising sustained long-term market growth. Competition among established players like Illumina, Thermo Fisher Scientific, and Agilent Technologies, alongside emerging innovative companies, drives further market dynamism and technological advancement. Recent developments include: February 2024: Vizzhy Inc. launched the world's inaugural Multiomics Lab in Bengaluru, India, heralding a major advancement in healthcare innovation. Equipped with cutting-edge tools and health AI technology, the lab enables physicians to pinpoint root causes and offer personalized recommendations for their patients.September 2023: MGI, a provider of technology and tools for life science, introduced the DCS Lab Initiative to stimulate crucial scientific research. This initiative encourages large-scale multiomics laboratories. Under the initiative, the organization offers products for numerous applications, including cell omics, DNA sequencing, and spatial omics based on DNBSEQ technologies, to specified research institutions globally.April 2023: Biomodal, formerly Cambridge Epigenetix, introduced a new duet multiomics solution that can enable simultaneous phased reading of epigenetic and genetic information in a single, low-volume sample.. Key drivers for this market are: Rising Demand for Single-cell Multiomics and Advancements in Omics Technologies, Increasing Investment in Genomics R&D; Growing Demand for Personalized Medicine. Potential restraints include: Rising Demand for Single-cell Multiomics and Advancements in Omics Technologies, Increasing Investment in Genomics R&D; Growing Demand for Personalized Medicine. Notable trends are: The Bulk Multiomics Segment is Expected to Hold the Largest Share of the Market.

Triple negative breast cancer is an aggressive type of breast cancer with very little treatment options. TNBC is very heterogeneous with large alterations in the genomic, transcriptomic, and proteomic landscapes leading to various subtypes with differing responses to therapeutic treatments. We applied a multi-omics data integration method to evaluate the correlation of important regulatory features in TNBC BRCA1 wild-type MDA-MB-231 and TNBC BRCA1 5382insC mutated HCC1937 cells compared with normal epithelial breast MCF10A cells. The data includes DNA methylation, RNAseq, protein, phosphoproteomics, and histone post-translational modification. Data integration methods identified regulatory features from each omics method had greater than 80% positive correlation within each TNBC subtype. Key regulatory features at each omics level were identified distinguishing the three cell lines and were involved in important cancer related pathways such as TGFbeta signaling, PI3K/AKT/mTOR, and Wnt/beta-catenin signaling.

Genetic background is a major driver of the phenotypic variability observed across pluripotent stem cells (PSCs), and studies addressing it have relied on transcript abundance as the primary molecular readout of cell state. However, little is known about how proteins, the functional units in the cell, vary across genetically diverse PSCs and how this relates to variation in other measures of gene output. Here we present the first comprehensive genetic study characterizing the pluripotent proteome using 190 unique mouse embryonic stem cell lines derived from highly heterogeneous Diversity Outbred mice. Moreover, we integrated the proteome with chromatin accessibility and transcript abundance in 163 cell lines with matching genotypes using multi-omics factor analysis to distinguish shared and unique drivers of variability across molecular layers. Our findings highlight the power of multi-omics data integration in revealing the distal impacts of genetic variation. We show that limitations in mapping of individual molecular traits may be overcome by utilizing data integration to consolidate the influence of genetic signals shared across molecular traits and increase detection power.

The mechanisms responsible for weight loss-induced improvement in insulin sensitivity are partially understood. Greater insight can now be achieved through deep phenotyping and data integration. Here, we used an integrative approach to investigate associations between changes in insulin sensitivity and variations in lifestyle factors (diet and physical activity), subcutaneous adipose tissue (sAT) gene expression, metabolomics in serum, urine and feces, and gut microbiota composition after a 6-week calorie restriction period in overweight and obese adults. A spectrum of variables from lifestyle factors, gut microbiota and host multi-omics most associated with insulin sensitivity was identified. These analyses highlight associations between variations in insulin sensitivity, serum branched chain amino acids, sAT genes involved in endoplasmic reticulum stress and ubiquitination, and gut metagenomic species. This work has enhanced previous knowledge on mechanistic links between host glucose homeostasis, lifestyle factors and microbiota, and has identified modifiable factors and biomarkers that may be used to predict and improve individual response to weight loss interventions.

Single Cell Multi-Omics Market Outlook

As of 2023, the global single cell multi-omics market size is valued at approximately USD 2.5 billion, with a robust projected CAGR of 20.1% forecasted to propel the market to USD 9.8 billion by 2032. This remarkable growth is driven by several key factors, including technological advancements in single-cell analysis techniques, increased funding for omics research, and a growing emphasis on personalized medicine. The market is experiencing a surge in demand as researchers and healthcare providers seek more precise and comprehensive insights into cellular behavior, disease mechanisms, and therapeutic responses. The integration of multi-omics data at a single-cell level offers unparalleled resolution and depth, enabling a transformative understanding of complex biological systems.

One of the primary growth drivers of the single cell multi-omics market is the rapid advancement of technology, particularly in sequencing and analytical tools. Innovations in microfluidics, next-generation sequencing, and enhanced bioinformatics capabilities have significantly lowered the cost and increased the efficiency of single-cell analysis. These technological advancements allow researchers to dissect the heterogeneity of cellular populations with unprecedented precision, facilitating breakthroughs in understanding disease pathology and developing targeted therapeutics. Moreover, the continuous evolution of these technologies fosters their adoption across various fields, further expanding the market's scope and application.

Another significant factor contributing to market growth is the escalating demand for personalized medicine. As the healthcare industry shifts towards more individualized treatment approaches, the need for comprehensive insights at a cellular level becomes paramount. Single cell multi-omics provides a holistic view of cellular function by integrating genomic, transcriptomic, proteomic, and metabolomic data. This integrated approach not only enhances the understanding of disease mechanisms but also aids in the development of personalized therapeutic strategies, thereby driving the adoption of single cell multi-omics in clinical settings. The ability to tailor treatments based on unique cellular profiles is expected to significantly boost market demand over the forecast period.

Additionally, increasing funding and investments in life sciences research is acting as a catalyst for the growth of the single cell multi-omics market. Governments, academic institutions, and private entities are investing heavily in omics research to unlock new scientific insights and address pressing healthcare challenges. This influx of funding is facilitating the establishment of state-of-the-art research facilities and fostering collaborations between academic institutions and industry players. The enhanced research infrastructure and collaborative efforts are expected to accelerate scientific discoveries and propel the market's expansion, as researchers strive to unravel the complexities of biological systems at a single-cell level.

From a regional perspective, North America currently dominates the single cell multi-omics market, owing to its robust research infrastructure, presence of leading biotechnology firms, and substantial government funding for genomics and precision medicine initiatives. However, the Asia Pacific region is anticipated to exhibit the highest growth rate over the forecast period, driven by increasing investments in healthcare research, the rising prevalence of chronic diseases, and the burgeoning biotechnology sector. European countries are also witnessing a growing adoption of single cell multi-omics technologies, supported by collaborative research initiatives and favorable regulatory frameworks. These regional dynamics underscore the diverse growth opportunities within the global market, as stakeholders capitalize on regional strengths and address specific healthcare needs.

Technology Analysis

The technology segment within the single cell multi-omics market is predominantly categorized into single cell genomics, single cell transcriptomics, single cell proteomics, and single cell metabolomics. Each of these sub-segments plays a crucial role in providing comprehensive insights into cellular functions and interactions. Single cell genomics, which involves the analysis of DNA at a single-cell level, has become a cornerstone technology in this market. It enables researchers to investigate genetic variations, mutations, and chromosomal aberrations with unprecedented accuracy. This technology is pivotal in advancing our understanding of genetic predisposit

We proposed DCCA for accurately dissecting the cellular heterogeneity on joint-profiling multi-omics data from the same individual cell by transferring representation between each other.

This repository contains the input and output files necessary to reproduce the case studies reported in the manuscript "MUUMI: an R package for statistical and network-based meta-analysis for MUlti-omics data Integration". MUUMI is an R package implementing network-based data integration and statistical meta-analysis within a single analytical framework. MUUMI allows the identification of robust molecular signatures through multiple meta-analytic methods, inference and analysis of molecular interactomes and the integration of multiple omics layers. The functionalities of MUUMI are showcased in two case studies in which we analysed 17 transcriptomic datasets on idiopathic pulmonary fibrosis (IPF) from both microarray and RNA-Seq platforms and multi-omics data of THP-1 macrophages exposed to different polarising stimuli. Part of the data reported in this repository derive from the Zenodo entry https://doi.org/10.5281/zenodo.10692129 (Curated and harmonised transcriptomics datasets of interstitial lung disease patients). Other data derive from the following publication: Migliaccio G, Morikka J, del Giudice G, Vaani M, Möbus L, Serra A, Federico A, Greco D. Methylation and transcriptomic profiling reveals short term and long term regulatory responses in polarized macrophages, Comp and Struct Biotech J, 2024(25), 143-152. doi: 10.1016/j.csbj.2024.08.018.

Multi-omics has the promise to provide a detailed molecular picture for biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimum structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to associate with a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains: Lentilactobacillus kefiri and Lactobacillus kefiranofaciens. We applied growth temperatures of 30°C and 37°C, and obtained transcriptomic, metabolomic, and proteomic data for the same 20 samples (10 samples per temperature). We obtained a multi-omics interaction network, which generated insights that would not have been possible with single-omics analysis. We identified interactions among transcripts, proteins, and metabolites suggesting active toxin/antitoxin systems. We also observed multifarious interactions that involved the shikimate pathway. These observations helped explain bacterial adaptation to different stress conditions, co-aggregation, and increased activation of L. kefiranofaciens at 37°C.

Results for the integrated analysis and various analysis.

We provide a single-nuclei RNA-sequencing (snRNA-seq) dataset derived from four COVID-19 patients, generated using the 10x Chromium Next GEM Single Cell v3.1 kit. For our study, these were integrated with snRNA-seq data with 12 publicly available sc/snRNA-seq datasets, comprising organ donor lung samples (n=89) and COVID-19 lung tissue samples (n=51). Additionally, we provide a spatial transcriptomic dataset characterizing different histopathological stages of diffuse alveolar damage, across a cohort of 33 COVID-19 patients. This was generated using the Nanostring Whole Transcriptome Assay (WTA) with regions of interest (ROIs) sized 400 µm² each. This integrated multi-omics analysis of snRNA-seq, histology, and spatial transcriptomics data, is available for exploration and download via our web portal: https://covid19-multiomicatlas.cellgeni.sanger.ac.uk/.

The Multiomics Market offers a range of products, including instruments, consumables, software, and services. Instruments include sequencing systems, mass spectrometers, and flow cytometers. Consumables encompass reagents, kits, and microarrays. Software solutions provide data analysis and visualization capabilities. Services include sample preparation, data analysis, and interpretation. Recent developments include: September 2023: The chromium single-cell gene expression flex assay manufactured by 10x Genomics Inc. now offers high throughput multi-omic cellular profiling as a commercially available capability thanks to the introduction of a new kit. Researchers and their options may detect simultaneous gene and protein expression, which can be expanded at a greater scale thanks to the new kit, which makes the multi-omic characterization of cell populations simple and efficient. The company's product portfolio was able to grow due to this technique., February 2023: Becton, Dickinson, and Company introduced the Rhapsody HT Xpress System, a high-throughput single-cell multiomics platform, to broaden the field of scientific research. With up to eight times more cells per sample than previous BD single-cell analyzers, this innovative technology allows scientists to extract, label, and analyze individual cells at a high sample throughput. This plan should assist the business in expanding its product's uses and serving more clients.. Notable trends are: Rising integration of multi-omics data is driving the market growth.

High-risk neuroblastoma is a very aggressive disease, with excessive tumor growth and poor outcomes. A proper stratification of the high-risk patients by prognostic outcome is important for treatment. However, there is still a lack of survival stratification for the high-risk neuroblastoma. To fill the gap, we adopt a deep learning algorithm, Autoencoder, to integrate multi-omics data, and combine it with K-means clustering to identify two subtypes with significant survival differences. By comparing the Autoencoder with PCA, iCluster, and DGscore about the classification based on multi-omics data integration, Autoencoder-based classification outperforms the alternative approaches. Furthermore, we also validated the classification in two independent datasets by training machine-learning classification models, and confirmed its robustness. Functional analysis revealed that MYCN amplification was more frequently occurred in the ultra-high-risk subtype, in accordance with the overexpression of MYC/MYCN targets in this subtype. In summary, prognostic subtypes identified by deep learning-based multi-omics integration could not only improve our understanding of molecular mechanism, but also help the clinicians make decisions.

Multi-omics datasets, including scRNA-seq, scATAC-seq, and CITE-seq, are used for integration with BiCLUM

Datasets for protein, mRNA, and microRNAThe file contains four datasets. Three with differentially expressed protein, mRNA, and microRNA, respectively. The last dataset contain data on all proteins detected in all TMT sets. Information about the sample names is included in the readme file.Datasets.xlsx

The biomarkers for thyroid cancer are still not known properly. For treating thyroid cancer these biomarkers can by be targeted specifically. Through this project, we identified and used bioinformatics tools to find biomarkers associated with thyroid cancer. Gene Expression Omnibus database (GEO) was used to find dataset related with thyroid cancer. Their expression profiles were downloaded. Four dataset GSE3467, GSE3678, GSE33630, and GSE53157 were identified from GEO database. The dataset GSE3467 contains nine thyroid tumor samples and nine normal thyroid tissue samples. The GSE3678 contains seven thyroid tumor samples and seven normal thyroid tissue samples. The GSE53157 contains twenty four thyroid tumor samples and three normal thyroid samples. The GSE33630 contains sixty thyroid tumor samples and forty five normal thyroid samples. These four datasets were analyzed individually and were integrated at the end to find the common genes among these four datasets. The microarray analysis of the datasets were performed using excel. T.Test analysis were performed for all the four datasets individually on a separate excel sheet. The data was normalized by converting normal value into log scale. Differential expression analysis of all the four datasets were done to identify differentially expresses genes (DEGs). Only upregulated genes were taken into account. Principal component analysis (PCA) of all the four dataset were performed using the raw data. The PCA analysis were performed using T-BioInfo server and the scatterplots were prepared using excel. RStudio was used to match the gene symbols with the corresponding probe ids using left join function. Inner join function in R was used to find integrated genes between the four datasets. Heatmaps of all the four datasets were performed using RStudio. To find number of intersection of Differentially expressed genes, an upset plot was prepared using RStudio. 74 genes with their corresponding probe ids were found to be common among all the four datasets. These genes are common to at least two datasets. These 74 common genes were analyzed using Database for Annotation, Visualization, and Integrated Discovery (DAVID), to study their Gene onotology (GO) functional annotations and pathways. According to the GO functional annotations result, most of the integrated upregulated genes were involved in protein binding, plasma membrane and integral component of membrane. Most common pathway include Extracellular matrix organization, Neutrophil degranulation, TGF-beta signaling pathway and Epithelial to mesenchymal transition in colorectal cancer. These 74 genes were introduced to STRING database to find protein-protein interactions between the genes. Interactions between the nodes were downloaded from STRING database and introduced to Sytoscape. Sytoscape analysis explained that only 19 genes showed protein-protein interactions between each other. Disease free survival analysis of the 13 genes that were common to three datasets were done using GEPIA. Boxplots of these 13 genes were also prepared using GEPIA. This showed that these differentially expressed genes showed different expression in normal thyroid tissue and thyroid tumor samples. Hence these 13 genes common to 3 datasets can be used as potential biomarkers for thyroid cancer. Among these 13 genes, four genes are implicated in cancer/cell proliferation can be probable target for treatment options.